QUESTION: 29

A data scientist has been given an incomplete notebook from the data engineering team. The notebook uses a Spark DataFrame spark_df on which the data scientist needs to perform further feature engineering. Unfortunately, the data scientist has not yet learned the PySpark DataFrame API.
Which of the following blocks of code can the data scientist run to be able to use the pandas API on Spark?

import pyspark.pandas as ps df = ps.DataFrame(spark_df)
import pyspark.pandas as ps df = ps.to_pandas(spark_df)
spark_df.to_sql()
import pandas as pd df = pd.DataFrame(spark_df)
spark_df.to_pandas()

Answer(s): A

Explanation:

To use the pandas API on Spark, which is designed to bridge the gap between the simplicity of pandas and the scalability of Spark, the correct approach involves importing the pyspark.pandas (recently renamed to pandas_api_on_spark) module and converting a Spark DataFrame to a pandas- on-Spark DataFrame using this API. The provided syntax correctly initializes a pandas-on-Spark DataFrame, allowing the data scientist to work with the familiar pandas-like API on large datasets managed by Spark.

Reference
Pandas API on Spark Documentation:
https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html

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QUESTION: 30

A data scientist has produced two models for a single machine learning problem. One of the models performs well when one of the features has a value of less than 5, and the other model performs well when the value of that feature is greater than or equal to 5. The data scientist decides to combine the two models into a single machine learning solution.
Which of the following terms is used to describe this combination of models?

Bootstrap aggregation
Support vector machines
Bucketing
Ensemble learning
Stacking

Answer(s): D

Explanation:

Ensemble learning is a machine learning technique that involves combining several models to solve a particular problem. The scenario described fits the concept of ensemble learning, where two models, each performing well under different conditions, are combined to create a more robust model. This approach often leads to better performance as it combines the strengths of multiple models.
Reference
Introduction to Ensemble Learning: https://machinelearningmastery.com/ensemble-machine- learning-algorithms-python-scikit-learn/

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QUESTION: 31

Which of the following machine learning algorithms typically uses bagging?

Gradient boosted trees
K-means
Random forest
Linear regression
Decision tree

Answer(s): C

Explanation:

Random Forest is a machine learning algorithm that typically uses bagging (Bootstrap Aggregating). Bagging involves training multiple models independently on different random subsets of the data and then combining their predictions. Random Forests consist of many decision trees trained on random subsets of the training data and features, and their predictions are averaged to improve accuracy and control overfitting. This method enhances model robustness and predictive performance.

Reference:

Ensemble Methods in Machine Learning (Understanding Bagging and Random Forests).

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QUESTION: 32

The implementation of linear regression in Spark ML first attempts to solve the linear regression problem using matrix decomposition, but this method does not scale well to large datasets with a large number of variables.
Which of the following approaches does Spark ML use to distribute the training of a linear regression model for large data?

Logistic regression
Spark ML cannot distribute linear regression training
Iterative optimization
Least-squares method
Singular value decomposition

Answer(s): C

Explanation:

For large datasets with many variables, Spark ML distributes the training of a linear regression model using iterative optimization methods. Specifically, Spark ML employs algorithms such as Gradient Descent or L-BFGS (Limited-memory BroydenFletcherGoldfarbShanno) to iteratively minimize the loss function. These iterative methods are suitable for distributed computing environments and can handle large-scale data efficiently by partitioning the data across nodes in a cluster and performing parallel updates.

Reference:

Spark MLlib Documentation (Linear Regression with Iterative Optimization).

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QUESTION: 29

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QUESTION: 32

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